The use of the visibility weights in the definition of the sampling function is called natural weighting as it is natural to weight each visibility by the inverse of noise variance. Natural weighting is also the way to maximize the point source sensitivity in the final image. However, the exact scaling of the sampling function is an additional degree of freedom of the imaging process. In particular, the user may change this scaling to give more or less weight to the large/short spatial frequencies.

We can thus introduce a weighting function in the definitions of
and

(4.4) |

(4.5) |

**Robust weighting**- In this case, is computed to enhance the
contribution of the large spatial frequencies. This is done by first
computing the natural weight in each cell of the plane. Then is
derived so that
- The product in a cell is set to a constant if the natural weight is larger that a given threshold;
- (
*i.e.*natural weighting) otherwise.

*i.e.*very low noise cells) while it keeps natural weighting of the noisy cells. It happens that the cells of the outer plane (corresponding to the large interferometer configurations) are often noisier that the cells of the inner plane (just because there are less cells in the inner plane). Robust weighting thus increase the spatial resolution by emphasizing the large spatial frequencies at the cost of a worst sensitivity to point sources... **Tapering**- is the apodization of the coverage by simple
multiplication of a Gaussian

(4.6) *i.e.*a smoothing of the result. The only purpose of this is to increase the sensitivity to extended structure. Tapering should almost never be used as this somehow implies that you throw away large spatial frequencies measured by the interferometer... In other words, use compact configuration of the arrays and not tapering to increase sensitivity to extended structures in your source.